Field of the Invention
The present invention generally relates to the field of image processing, and more particularly, relates to method and apparatus for generating a multi-class classifier, method and apparatus for updating a multi-class classifier, method and apparatus for detecting objects as well as image processing device.
Description of the Related Art
Object detection as a main research aspect in computer vision has achieved considerable development in last few decades. Most of methods rely on collecting plenty of positive and negative samples for learning a binary-class classifier to identify objects and non-objects. But in some applications such as general object detection, image retrieval etc., they may be invalid because it is difficult to collect plenty of samples in advance.
One common solution is to learn a one-class classifier by one or few positive samples to distinguish objects from all other possible objects when user has given the object samples. One main problem of the one-class classification is that, in comparison to other solutions such as binary-class or multi-class classification, objects may be weakly discriminated, because no contrast samples could be used. Thus, it will result in high false detection rate.
For solving this problem, some methods based on background modeling were proposed. If some negative samples can be collected from background image regions, object detection may be treated as a binary classification task to distinguish objects from background, which can improve the classification accuracy under current background. Most of methods in current literatures, for example, the method given by a paper titled “Semi-Supervised On-line Boosting for Robust Tracking” (Helmut Grabner, Christian Leistner, and Horst Bischof. ECCV, 2008), are to learn a binary-class classifier by object samples and background samples firstly, and then to update the classifier by newly collected samples.
However, this kind of method cannot be used in some applications, for an example, shooting a running dog with a hand-hold camera, since there exist several issues in the prior art.
For an example, one issue lies in that, it needs to learn a binary-class classifier from object samples and background samples. When the background information cannot play a positive role (for an example, the object goes into an absolutely different scene), it cannot remove background classifier freely.
For another example, another issue lies in that, the learned binary-class classifier is only suitable for the fixed background or slowly changing background. If the background changes suddenly, it could not achieve very good results in object detection.